{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled42.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMRBcSw8m-UF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Description: This program classifies images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xq7j7GGtnGpg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5e85837b-c474-4404-f0a5-f32f6bd0d175"
      },
      "source": [
        "#Load the data\n",
        "from keras.datasets import cifar10\n",
        "(x_train, y_train), (x_test,y_test) = cifar10.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPxL5eqInb1S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "9a6c7aae-823f-4cd9-f090-700d68369f1c"
      },
      "source": [
        "#Get the data types\n",
        "print(type(x_train))\n",
        "print(type(y_train))\n",
        "print(type(x_test))\n",
        "print(type(y_test))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vY2l8oasnm62",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "3435ebff-9927-4dbd-eee8-c69c700a7387"
      },
      "source": [
        "#Get the shape of the data\n",
        "print('x_train shape:', x_train.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('x_test shape:', x_test.shape)\n",
        "print('y_test shape:', y_test.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (50000, 32, 32, 3)\n",
            "y_train shape: (50000, 1)\n",
            "x_test shape: (10000, 32, 32, 3)\n",
            "y_test shape: (10000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j2lTeINoEG4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "outputId": "9139dbe1-cb8b-4f4c-9ac5-742b06092398"
      },
      "source": [
        "#Take a look at the first image in the training data set\n",
        "x_train[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 59,  62,  63],\n",
              "        [ 43,  46,  45],\n",
              "        [ 50,  48,  43],\n",
              "        ...,\n",
              "        [158, 132, 108],\n",
              "        [152, 125, 102],\n",
              "        [148, 124, 103]],\n",
              "\n",
              "       [[ 16,  20,  20],\n",
              "        [  0,   0,   0],\n",
              "        [ 18,   8,   0],\n",
              "        ...,\n",
              "        [123,  88,  55],\n",
              "        [119,  83,  50],\n",
              "        [122,  87,  57]],\n",
              "\n",
              "       [[ 25,  24,  21],\n",
              "        [ 16,   7,   0],\n",
              "        [ 49,  27,   8],\n",
              "        ...,\n",
              "        [118,  84,  50],\n",
              "        [120,  84,  50],\n",
              "        [109,  73,  42]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[208, 170,  96],\n",
              "        [201, 153,  34],\n",
              "        [198, 161,  26],\n",
              "        ...,\n",
              "        [160, 133,  70],\n",
              "        [ 56,  31,   7],\n",
              "        [ 53,  34,  20]],\n",
              "\n",
              "       [[180, 139,  96],\n",
              "        [173, 123,  42],\n",
              "        [186, 144,  30],\n",
              "        ...,\n",
              "        [184, 148,  94],\n",
              "        [ 97,  62,  34],\n",
              "        [ 83,  53,  34]],\n",
              "\n",
              "       [[177, 144, 116],\n",
              "        [168, 129,  94],\n",
              "        [179, 142,  87],\n",
              "        ...,\n",
              "        [216, 184, 140],\n",
              "        [151, 118,  84],\n",
              "        [123,  92,  72]]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdR4-cyqoU4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "859659f2-27a3-4e11-8766-f98c849e5f7f"
      },
      "source": [
        "#Show the image as a picture\n",
        "import matplotlib.pyplot as plt\n",
        "img = plt.imshow(x_train[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jwaVWa4YT1Mx54lqzGQ7dyy31/QWAewPLv+ru+7r/egp8IcTVwwWD390fB8DLxQoh3pFc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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nzZuIG9olIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0b45f224-2392-44cd-f2f2-6eb87b1178da"
      },
      "source": [
        "#Print the label of the first image of the training data set\n",
        "print('The label is:', y_train[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The label is: [6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IT36ETxho6IC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "d1287fff-91b0-4fcb-b55c-ac52440126b1"
      },
      "source": [
        "#One Hot Encoding: Convert the labels into a set of 10 numbers to input into the neural network\n",
        "from keras.utils import to_categorical\n",
        "y_train_one_hot = to_categorical(y_train)\n",
        "y_test_one_hot = to_categorical(y_test)\n",
        "\n",
        "# print the new labels in the training data set\n",
        "print(y_train_one_hot)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 1.]\n",
            " [0. 0. 0. ... 0. 0. 1.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 1.]\n",
            " [0. 1. 0. ... 0. 0. 0.]\n",
            " [0. 1. 0. ... 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcGEt3IBpalN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Normalize the pixels in the images  to be values between 0 and 1\n",
        "x_train = x_train / 255\n",
        "x_test = x_test /255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbWwolFzpy7B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "82c3e46b-431a-4979-820b-e4ad56ed9723"
      },
      "source": [
        "#Build the CNN\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "\n",
        "#Create the architecture\n",
        "model = Sequential()\n",
        "\n",
        "#Convolution layer to extract features from input image\n",
        "model.add(Conv2D(32, (5,5), activation='relu', input_shape=(32,32,3)))\n",
        "\n",
        "#Pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "#Convolution layer to extract features from input image\n",
        "model.add(Conv2D(32, (5,5), activation='relu'))\n",
        "\n",
        "#Pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "#Flatten the image layer\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(1000, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0711 20:13:27.358891 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0711 20:13:27.377387 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0711 20:13:27.381951 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0711 20:13:27.401737 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2V1G3xiq2oA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "0b58c125-e6a3-444d-f8ef-4ef87fa7861d"
      },
      "source": [
        "#Compile the model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer = 'adam',\n",
        "    metrics =['accuracy']\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0711 20:14:45.216073 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0711 20:14:45.247650 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agaRNjmsrJi1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "17c85ac0-6104-49da-ba62-8eb59a328ebc"
      },
      "source": [
        "#Train the model\n",
        "hist = model.fit(x_train, y_train_one_hot, batch_size=256, epochs=10, validation_split=0.3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0711 20:16:39.325152 140453025163136 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0711 20:16:39.514693 140453025163136 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 35000 samples, validate on 15000 samples\n",
            "Epoch 1/10\n",
            "35000/35000 [==============================] - 52s 1ms/step - loss: 1.7251 - acc: 0.3731 - val_loss: 1.4676 - val_acc: 0.4721\n",
            "Epoch 2/10\n",
            "35000/35000 [==============================] - 52s 1ms/step - loss: 1.3876 - acc: 0.5040 - val_loss: 1.3376 - val_acc: 0.5200\n",
            "Epoch 3/10\n",
            "35000/35000 [==============================] - 52s 1ms/step - loss: 1.2483 - acc: 0.5569 - val_loss: 1.2679 - val_acc: 0.5554\n",
            "Epoch 4/10\n",
            "35000/35000 [==============================] - 52s 1ms/step - loss: 1.1491 - acc: 0.5964 - val_loss: 1.1519 - val_acc: 0.5964\n",
            "Epoch 5/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 1.0621 - acc: 0.6268 - val_loss: 1.1007 - val_acc: 0.6175\n",
            "Epoch 6/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 0.9819 - acc: 0.6567 - val_loss: 1.0911 - val_acc: 0.6180\n",
            "Epoch 7/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 0.9281 - acc: 0.6753 - val_loss: 1.0711 - val_acc: 0.6257\n",
            "Epoch 8/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 0.8519 - acc: 0.7057 - val_loss: 0.9948 - val_acc: 0.6565\n",
            "Epoch 9/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 0.7965 - acc: 0.7224 - val_loss: 0.9797 - val_acc: 0.6675\n",
            "Epoch 10/10\n",
            "35000/35000 [==============================] - 53s 2ms/step - loss: 0.7398 - acc: 0.7449 - val_loss: 0.9726 - val_acc: 0.6683\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrRkD36kuB1x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "23c05035-2923-4a6b-fbe2-aad1b467db3c"
      },
      "source": [
        "#Evaluate the model\n",
        "model.evaluate(x_test, y_test_one_hot)[1]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 5s 486us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6656"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8Hg6aNUuWlv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3824ef24-65f6-408a-bf73-133bf30bdab8"
      },
      "source": [
        "#Visualize the models accuracy\n",
        "plt.plot(hist.history['acc'])\n",
        "plt.plot(hist.history['val_acc'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x4MdODaJDf0sQxtQBvzu4o4BewCggEVggIgNrerKqTgWmAqSmpqoXARp/LP/o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\nGGOMqdb/A7w8HVAmE8GZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1-KKo_YvB9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "baa4e3c0-800c-4bf0-dac6-8cf661759189"
      },
      "source": [
        "#Visualize the models accuracy\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Yy+PDupDQMsrv0vxVkA9HUrxxHN+8CPvXQP3mcNl4SLpX04XUYAoFkTI4ciKP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\nBRERKaZQEBGRYgoFEREpplAQEZFi/x9dSCNS1DLgxQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66_qgsqTvWnf",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a10337a7-396e-4a46-c426-6da9c17fe4d4"
      },
      "source": [
        "#Load data to make classifications\n",
        "#from google.colab import files\n",
        "#uploaded = files.upload()\n",
        "my_image = plt.imread('cat.4014.jpg')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0734d09d-9cde-4ff7-a0cf-aa2da6688dfb\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-0734d09d-9cde-4ff7-a0cf-aa2da6688dfb\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving cat.4014.jpg to cat.4014.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99cElRyMvoIP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "5c794a59-5620-4741-d562-9c4511747ece"
      },
      "source": [
        "#Show the uploaded image\n",
        "img = plt.imshow(my_image)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XvsbRwyO6rmO73bLdbmlePkfWJVIafPuAupwxf1AjlHgTz7IxIYGCEYMmS7OC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ui8hvicjnROQfichfjsffV30VkVpEfkdE/jD28z+Mxz8oIr8d+/M/iEgZj1fx85fj96++l+1/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\nz0D/Pz6l25v78KYsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoP9-oqav1Pl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "bfdf267d-e84f-4fd3-cf76-29594c7ed7c4"
      },
      "source": [
        "#Resize the image\n",
        "from skimage.transform import resize\n",
        "my_image_resized = resize(my_image, (32,32,3))\n",
        "img = plt.imshow(my_image_resized)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xsRn4cTQoJPrAY09T2/j4L6ntqqs/QG0jq9MybCQ7VxWLl97z+nTnFyJTFPxCZIqCX4hM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUAmcIswwMFe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "ba98d040-0d4c-4bcd-be09-50b5b55231fe"
      },
      "source": [
        "#Get the probabilities for each class\n",
        "import numpy as np\n",
        "probabilities = model.predict( np.array([my_image_resized,]) )\n",
        "#Show the probability for each class\n",
        "probabilities"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.0473169e-03, 5.8718451e-06, 9.6833229e-02, 8.5426587e-01,\n",
              "        2.0653252e-02, 4.0493100e-03, 2.2059367e-03, 1.5260294e-02,\n",
              "        5.5901473e-04, 4.1198554e-03]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K847uAzhw8E_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "36746dd6-2634-4c5c-fd93-95ff3b84fe90"
      },
      "source": [
        "number_to_class = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "index = np.argsort(probabilities[0,:])\n",
        "\n",
        "#Print the first 5 most likely classes / labels\n",
        "print('Most likely class:', number_to_class[index[9]], \"--Probability:\", probabilities[0, index[9]])\n",
        "print('Second most likely class:', number_to_class[index[8]], \"--Probability:\", probabilities[0, index[8]])\n",
        "print('Third most likely class:', number_to_class[index[7]], \"--Probability:\", probabilities[0, index[7]])\n",
        "print('Fourth most likely class:', number_to_class[index[6]], \"--Probability:\", probabilities[0, index[6]])\n",
        "print('Fifth most likely class:', number_to_class[index[5]], \"--Probability:\", probabilities[0, index[5]])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Most likely class: cat --Probability: 0.85426587\n",
            "Second most likely class: bird --Probability: 0.09683323\n",
            "Third most likely class: deer --Probability: 0.020653252\n",
            "Fourth most likely class: horse --Probability: 0.015260294\n",
            "Fifth most likely class: truck --Probability: 0.0041198554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lkLGa-vyfmC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save the model\n",
        "model.save('my_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyRmjgS5ylD-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#To load the model\n",
        "from keras.models import load_model\n",
        "model = load_model('my_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
